While pyannote.audio supports training models from the waveform directly (e.g. using SincNet learnable features [1]}), the pyannote.audio.features module provides a collection of standard feature extraction techniques such as MFCCs or spectrograms using the implementation available in the librosa library [2]}. They all inherit from the same FeatureExtraction base class that supports on-the-fly data augmentation which is very convenient for training neural networks. For instance, it supports extracting features from random audio chunks while applying additive noise from databases such as MUSAN [3]}. Contrary to other tools that generate in advance a fixed number of augmented versions of each original audio file, pyannote.audio generates a virtually infinite number of versions as the augmentation is done on-the-fly every time an audio chunk is processed.
